Researchers Have Developed An Algorithm For Taxi Sharing That Could Revolutionise City Transport

Researchers have developed a taxi sharing method which calculates options within a minute of a ride request without having to re-route already started trips.

Tested in New York City using the data from 13,000 New York City cabs over one year, the scientists say the algorithm will ease traffic congestion, reduce service costs, cut pollution and allow riders to split fares.

Carlo Ratti of Massachusetts Institute of Technology and colleagues developed a mathematical model which produces a shareability network to identify the collective benefits of taxi sharing.

The authors applied their method to a data set of 150 million taxi trips and identified opportunities for trip sharing with minimal passenger discomfort and without re-routing already started trips.

If passengers had been willing to tolerate no more than five minutes in delays per trip, almost 95% of the trips could have been shared.

The optimal combination of trips would have reduced total travel time by 40%, with corresponding reductions in operational costs and carbon dioxide emissions.

The authors estimate that systems employing the method would allow taxi companies to calculate sharing options within 1 minute of a ride request.

“Of course, nobody should ever be forced to share a vehicle,” says Ratti, professor of the practice in MIT’s Department of Urban Studies and Planning.

“However, our research shows what would happen if people have sharing as an option. This is more than a theoretical exercise, with services such as Uber Pool (carpooling) bringing these ideas into practice.”

An online application, HubCab, allows people to explore the taxi data themselves, using a map of New York as an interface.

This video explains the data:

The study by researchers at MIT, Cornell University and the Italian National Research Council’s Institute for Informatics and Telematics, is published in the journal PNA (Proceedings of the National Academies of Sciences).